code: def addmv(input, mat, vec, *, beta=1, alpha=1, out=None):
      if len(ivy.shape(mat)) != 2 or len(ivy.shape(vec)) != 1:
          raise RuntimeError("input must be 2D matrix and 1D vector")
      mat, vec = torch_frontend.promote_types_of_torch_inputs(mat, vec)
      ret = ivy.matmul(mat, vec, out=out)
      alpha, ret = torch_frontend.promote_types_of_torch_inputs(
          alpha, ret
      )
      ret = ivy.multiply(alpha, ret, out=out)
      beta, input = torch_frontend.promote_types_of_torch_inputs(
          beta, input
      )
      beta_input = ivy.multiply(beta, input, out=out)
      beta_input, ret = torch_frontend.promote_types_of_torch_inputs(
          beta_input, ret
      )
      return ivy.add(beta_input, ret, out=out)
  
imports: import ivy
  from ivy.func_wrapper import with_unsupported_dtypes
  import ivy.functional.frontends.torch
  from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
label_desc: Use image node_id 3 for calling a global function with example usage: addmv(input, mat, vec) and returns: ivy